Impact of Knowledge, Tendency and Perceived Threats of Climate Change on Adaptation Strategies: The Case of Tehran Architects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The consequences of climate change are observed in several ways in human settlements, one of which is the threat it poses to the physical elements and infrastructures of cities. To mitigate it, cities apply adaptation strategies. These strategies have proper effectiveness and are adapted according to local characteristics. This study applied the cross-sectional survey method and Structural Equation Modeling (SEM) to assess the possible relations between variables. The study population was the architects of Tehran metropolis with a sample size of 85. The study instrument was a researcher-developed questionnaire consisting of four sections. Five hypotheses were assessed for relations of knowledge, tendency, perceived threats, and the adaptation strategies, all of which were proved by the study results. The results of the study showed that knowledge on the climate change significantly affects the perceived threats, tendency and the adaptation strategies. The adaptation strategies were also dependent on tendency and the perceived threats. The findings of this study can be helpful for planners and decision makers and the Architecture Society of Tehran to address the problem of climate change more adequately.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it